Techniques are disclosed for managing a network. In one example, a device configuration manager is configured to generate, in accordance with a device management protocol, a configuration change request representing a transaction having a first sub-transaction specifying a first configuration change for a network device of the network and a second sub-transaction specifying a second configuration change for the same network device. The device configuration manager is further configured to output the configuration change request to the network device and receive a reply message from the network device. The reply message includes a first response element specifying whether the first configuration change is successfully committed at the network device and a second response element specifying whether the second configuration change is successfully committed at the network device.
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6. The method of claim 1, wherein the processing circuitry is arranged in a network management system (NMS).
A network management system (NMS) is used to monitor, configure, and manage network devices and services. A challenge in NMS design is efficiently processing and analyzing large volumes of network data to detect anomalies, optimize performance, and ensure security. Traditional systems often struggle with scalability, real-time processing, and adaptive decision-making. This invention improves NMS functionality by incorporating specialized processing circuitry designed to handle network data efficiently. The circuitry is optimized for tasks such as traffic analysis, anomaly detection, and policy enforcement. It includes modules for real-time data ingestion, pattern recognition, and automated response mechanisms. The system dynamically adjusts its processing parameters based on network conditions, improving adaptability and reducing latency. The processing circuitry integrates with existing NMS components, such as monitoring agents and configuration tools, to enhance overall system performance. It supports distributed processing, allowing parallel execution of tasks across multiple nodes. The system also includes machine learning algorithms to refine detection accuracy and predictive capabilities over time. By embedding this circuitry within the NMS, the invention enables faster, more accurate network management while reducing computational overhead. The solution is particularly useful in large-scale networks where traditional processing methods are insufficient. The system ensures continuous monitoring, proactive issue resolution, and improved network reliability.
8. The method of claim 7, wherein dividing the batch of configuration changes is based on an error message indicated in the second reply message.
A system and method for managing configuration changes in a computing environment involves processing a batch of configuration changes to ensure reliable deployment. The method includes receiving a batch of configuration changes from a client device, sending the batch to a server for processing, and receiving a reply message indicating whether the batch was successfully processed. If the batch fails, the system divides the batch into smaller subsets of configuration changes and resends each subset individually to the server for reprocessing. The division of the batch is based on error messages received in the reply message, allowing the system to isolate and retry failed changes while successfully processed changes remain unaffected. This approach improves the reliability of configuration deployments by reducing the risk of large-scale failures and enabling targeted retries of problematic changes. The method ensures that only the necessary changes are reprocessed, minimizing unnecessary workload and improving efficiency. The system may also include a client device that initiates the configuration changes and a server that processes them, with communication between the devices facilitated through network protocols. The method is particularly useful in environments where configuration changes must be applied consistently and reliably, such as in cloud computing, enterprise IT systems, or distributed computing environments.
15. The system of claim 10, wherein the system is a network management system (NMS).
A network management system (NMS) is designed to monitor, control, and optimize the performance of networked devices and infrastructure. Traditional NMS solutions often struggle with scalability, real-time data processing, and efficient resource allocation, leading to inefficiencies in network operations. This system addresses these challenges by integrating advanced monitoring, automation, and analytics capabilities to enhance network reliability and performance. The system includes a centralized management platform that collects and processes data from various network devices, such as routers, switches, and servers. It employs real-time monitoring to detect anomalies, performance degradation, or potential failures, allowing for proactive issue resolution. Automated workflows are used to streamline routine tasks, such as configuration management, fault detection, and capacity planning, reducing manual intervention and human error. The system also incorporates predictive analytics to forecast network traffic patterns and resource demands, enabling dynamic adjustments to optimize performance. Additionally, it supports multi-vendor environments, ensuring compatibility with diverse network hardware and software. By providing a unified interface for network administration, the system simplifies operations, improves visibility, and enhances decision-making for network administrators. This approach ensures efficient network management, minimizes downtime, and supports scalable growth in complex network infrastructures.
17. The system of claim 16, wherein dividing the batch of configuration changes is based on an error message indicated in the second reply message.
A system for managing configuration changes in a computing environment addresses the challenge of efficiently processing large batches of configuration updates while minimizing disruptions. The system receives a batch of configuration changes and divides them into smaller subsets to reduce the risk of system failures during deployment. Each subset is processed sequentially, with the system monitoring responses from the target computing environment to determine the success or failure of each subset. If an error occurs during the processing of a subset, the system analyzes error messages in the response to identify the cause and adjusts subsequent processing accordingly. This adaptive approach ensures that configuration changes are applied in a controlled manner, reducing the likelihood of widespread system outages. The system may also prioritize subsets based on their impact or urgency, further optimizing the deployment process. By dynamically adjusting the division of configuration changes based on error feedback, the system improves reliability and maintainability in large-scale computing environments.
20. The non-transitory computer-readable media of claim 19, wherein the processing circuitry is arranged in a network management system (NMS).
A network management system (NMS) is used to monitor and control network devices, but existing systems often lack efficient methods for processing and analyzing large volumes of network data in real time. This can lead to delays in identifying and resolving network issues, reducing overall network performance and reliability. The invention provides a solution by implementing a network management system (NMS) with processing circuitry designed to handle network data more effectively. The processing circuitry is configured to receive network data from multiple network devices, analyze the data to detect anomalies or performance issues, and generate alerts or take corrective actions automatically. The system may also include machine learning algorithms to improve detection accuracy over time. By integrating these capabilities into the NMS, the system can proactively manage network health, reduce downtime, and enhance operational efficiency. The processing circuitry may be further optimized for scalability, allowing the system to handle increasing data volumes without performance degradation. This approach ensures that network administrators can maintain high levels of service quality and quickly respond to emerging issues.
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March 29, 2022
May 7, 2024
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